Simo M. A. Goddijn (1), Guus de Krom (2)

In this study, Hidden Markov Models (HMMs) were used to evaluate
pronunciation. Native and non-native speakers were asked to
pronounce ten Dutch words. Each word was subsequently evaluated
by an expert listener. Her main task was to decide whether a word was
spoken by a native or a non-native speaker. For each word type, two
versions of prototype HMMs were defined: one to be trained on
tokens produced by a single native speaker, and another to be trained
on tokens produced by a group of native speakers. For testing the
different types of HMM, forced recognition was performed using
native and non-native judged tokens. We expected that recognition
with multi- speaker HMMs would allow a more effective
discrimination between native and non-native tokens than recognition
with single-speaker models. A comparison of Equal Error Rates partly
confirmed this hypothesis.